1. Field of the Invention
This disclosure generally relates to financial data processing, and in particular it relates to credit scoring, customer profiling, customer product offer targeting, and commercial credit behavior analysis and modeling.
2. Background Art
For the purposes of this disclosure, the term “business” will refer to non-publicly traded business entities, such as middle market commercial entities, franchises, small business corporations and partnerships, and sole proprietorships, as well as principals of these business entities. It is axiomatic that consumers and/or businesses will tend to spend more when they have greater purchasing power. The capability to accurately estimate a business's or a consumer's spend capacity could therefore allow a financial institution (such as a credit company, lender or any consumer or business services companies) to better target potential prospects and identify any opportunities to increase business to business (“B2B”) or business to consumer (“B2C”) transaction volumes, without an undue increase in the risk of defaults. Attracting additional consumer and/or commercial spending in this manner, in turn, would increase such financial institution's revenues, primarily in the form of an increase in transaction fees and interest payments received. Consequently, a model that can accurately estimate purchasing power is of paramount interest to many financial institutions and other financial services companies.
A limited ability to estimate spend behavior for goods and services that a business or consumer purchases has previously been available. A financial institution can, for example, simply monitor the balances of its own customers' accounts. When a credit balance is lowered, the financial institution could then assume that the corresponding customer now has greater purchasing power. However, it is often difficult to confirm whether the lowered balance is the result of a balance transfer to another account. Such balance transfers represent no increase in the customer's capacity to spend, and so this simple model of customer behavior has its flaws.
In order to achieve a complete picture of any customer's purchasing ability, one must examine in detail the full range of a customer's financial accounts, including credit accounts, checking and savings accounts, investment portfolios, and the like. However, the vast majority of customers do not maintain all such accounts with the same financial institution and the access to detailed financial information from other financial institutions is restricted by privacy laws, disclosure policies and security concerns.
There is limited and incomplete consumer information from credit bureaus and the like at the aggregate and individual consumer levels. Since balance transfers are nearly impossible to consistently identify from the face of such records, this information has not previously been enough to obtain accurate estimates of a consumer's actual spending ability.
Similarly, it would be useful for a financial institution to identify spend availability for corporate consumers, such as businesses and/or a principal of a business entity. Such an identification would allow the financial institution to accurately target the corporate businesses and/or principals most likely to have spend availability, and those most likely to increase their plastic spend on transactional accounts related to the financial institution. However, there is also limited data on corporate spend information, and identifying and predicting the size and share of a corporate wallet is difficult.
Accordingly, there is a need for a method and apparatus for modeling individual and corporate consumer spending behavior which addresses certain problems of existing technologies.